Romualdo Pastor-Satorras

Habituation plays a key role in shaping the collective behavior of moving animal groups, yet the effects of variation in habituation within groups are unknown. Here, we merge two subgroups of fish with different levels of habituation and quantitatively analyze their movement. Non-habituated fish (not familiar with their swimming environment) tend to occupy more central positions within the group and position themselves closer to neighbors, consistent with the selfish herd hypothesis. This behavior appears to be driven by adjusted effective local interactions with nearby individuals. Compared to more habituated fish, these individuals also exhibit greater coordination, shorter burst-and-coast dynamics, more efficient information transmission, and a stronger tendency to adopt follower roles. To validate the findings, we develop a machine learning tool that successfully classifies the subgroup identity of individual fish. Our study demonstrates how differences in local-scale inter-individual interactions can drive spatial assortment within groups, based on heterogeneity in their manipulated exposure to the experimental setup.

Multi-agent systems of large language models (LLMs) are rapidly expanding across domains, introducing dynamics not captured by single-agent evaluations. Yet, existing work has mostly contrasted the behavior of a single agent with that of a collective of fixed size, leaving open a central question: How does group size shape dynamics? Here, we move beyond this dichotomy and systematically explore outcomes across the full range of group sizes. We focus on multi-agent misalignment, building on recent evidence that interacting LLMs playing a simple coordination game can generate collective biases absent in individual models. First, we show that collective bias is a deeper phenomenon than previously assessed: Interaction can amplify individual biases, introduce new ones, or override model-level preferences. Second, we demonstrate that group size affects the dynamics in a nonlinear way, revealing model-dependent dynamical regimes. Finally, we develop a mean-field analytical approach and show that, above a critical population size, simulations converge to deterministic predictions that expose the basins of attraction of competing equilibria. These findings establish group size as a key driver of multi-agent dynamics and highlight the need to consider population-level effects when deploying LLM-based systems at scale.

Behavioral adoptions of individuals are influenced by their peers in different ways. While in some cases an individual may change behavior after a single incoming influence, in other cases multiple cumulated attempts of social influence are necessary for the same outcome. These two mechanisms, known as simple and complex contagion, often occur together in social contagion phenomena, yet their distinguishability based on the observable contagion dynamics is challenging. In this paper we define a social contagion model evolving on temporal networks where individuals can switch between contagion mechanisms. We explore three spreading scenarios: predominated by simple or complex contagion, or where the dominant mechanism changes during the unfolding process. We propose analytical and numerical methods relying on global spreading observables to identify which of these three scenarios characterizes a social spreading outbreak. This work offers insights into social contagion dynamics on temporal networks, without assuming prior knowledge about the contagion mechanism driving the adoptions of individuals.

Has ideological polarization actually increased in the past decades, or have voters simply sorted themselves into parties matching their ideology more closely? Here we present a methodology to quantify multidimensional ideological polarization by embedding the respondents to a wide variety of political, social and economic topics from the American National Election Studies into a two-dimensional ideological space. By identifying several demographic attributes of the American National Election Studies respondents, we chart how political and socioeconomic groups move through the ideological space in time. We observe that income and especially racial groups align into parties, but their ideological distance has not increased over time. Instead, Democrats and Republicans have become ideologically more distant in the past 30 years: Both parties moved away from the centre, at different rates. Furthermore, Democratic voters have become ideologically more heterogeneous after 2010, indicating that partisan sorting has declined in the past decade.

The precise modulation of activity through inhibitory signals ensures that both insect colonies and neural circuits operate efficiently and adaptively, highlighting the fundamental importance of inhibition in biological systems. Modulatory signals are produced in various contexts and are known for subtly shifting the probability of receiver behaviors based on response thresholds. Here we propose a non-linear function to introduce inhibitory responsiveness in collective decision-making inspired by honeybee house-hunting. We show that, compared with usual linear functions, non-linear responses enhance final consensus and reduce deliberation time. This improvement comes at the cost of reduced accuracy in identifying the best option. Nonetheless, for value-based tasks, the benefits of faster consensus and enhanced decision-making might outweigh this drawback.

Static and dynamic stress changes in the Earth's crust induced by an earthquake typically trigger other earthquakes. Identifying such aftershocks is an important step in seismic hazard assessment but has remained challenging, especially in cases involving natural fluid migration or anthropogenic fluid injections, which can occur with varying time scales and/or episodically, leading to strong temporal variations in earthquake occurrences. Here, we demonstrate analytically and numerically that earthquake catalogs without triggering can lead to spurious Omori-Utsu and productivity relations for the commonly used nearest-neighbor declustering method. However, we show that the robustness of the Omori-Utsu exponent on newly introduced parameters of the method allows one to determine whether, indeed, aftershocks are present. For a natural swarm catalog, which lacks a clear distinction between triggered and background events, we find that the catalog is dominated by aftershocks.

Moving animal groups transmit information through propagating waves or behavioral cascades, exhibiting characteristics akin to systems near a critical point from statistical physics. Using data from freely swimming schooling fish in an experimental tank, we investigate spontaneous behavioral cascades involving turning avalanches, where large directional shifts propagate across the group. We analyze several avalanche metrics and provide a detailed picture of the dynamics associated with turning avalanches, employing tools from avalanche behavior in condensed-matter physics and seismology. Our results identify power-law distributions and robust scale-free behavior through data collapses and scaling relationships, confirming a necessary condition for criticality in fish schools. We explore the biological function of turning avalanches and link them to collective decision-making processes in selecting a new movement direction for the school. We report relevant boundary effects arising from interactions with the tank walls and influential roles of boundary individuals. Finally, spatial and temporal correlations in avalanches are explored using the concept of aftershocks from seismology, revealing clustering of avalanche events below a designated timescale and an Omori law with a faster decay rate than observed in earthquakes. Published by the American Physical Society 2024

Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion, and alignment. Such forces can be inferred using “force maps,” i.e., by analyzing the dependency of the acceleration of a focal individual on relevant variables. Here, we introduce a force map technique suitable for the analysis of the alignment forces experienced by individuals. After validating it using an agent-based model, we apply the force map to experimental data of schooling fish. We observe signatures of an effective alignment force with faster neighbors and an unexpected antialignment with slower neighbors. Instead of an explicit antialignment behavior, we suggest that the observed pattern is the result of a selective attention mechanism, where fish pay less attention to slower neighbors. This mechanism implies the existence of temporal leadership interactions based on relative speeds between neighbors. We present support for this hypothesis both from agent-based modeling as well as from exploring leader–follower relationships in the experimental data.

Understanding the dynamics of opinion depolarization is pivotal to reducing the political divide in our society. We propose an opinion dynamics model, which we name the social compass model, for interdependent topics represented in a polar space, where zealots holding extreme opinions are less prone to change their minds. We analytically show that the phase transition from polarization to consensus, as a function of increasing social influence, is explosive if topics are not correlated. We validate our theoretical framework through extensive numerical simulations and recover explosive depolarization also by using initial opinions from the American National Election Studies, including polarized and interdependent topics.